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Dibenzyl toluene is a famous liquid organic hydrogen carrier and can be applied for energy storage regarding its reversible hydrogenation. In the present article, a complete explanation of a powerful particle swarm optimization (PSO) algorithm coupled with adaptive neuro fuzzy interface system (ANFIS) strategy was utilized to account for the solubility of methane, nitrogen, and carbon dioxide based on temperature, pressure, and molecular weight of solute. Modeling results indicate that predicted values of solubility are in good agreement with extracted experimental datasets. Also, accuracy and robustness of ANFIS-PSO strategy used for the prediction of solubility of methane, carbon dioxide, and nitrogen are comprehensible from comparing modeling results with experimental measurements. Therefore, ANFIS strategy can be employed as an assistive lever for engineers in the biorefineries for the prediction of the solubility of coproduct of methane steam reforming. Moreover, results illustrate that trends of solubility of aforementioned component in dibenzyl toluene are carbon dioxide&gt; methane&gt; nitrogen.

Toward modeling of solubility of carbon dioxide, methane, and nitrogen in liquid dibenzyl toluene (LDT) using rigorous technique